US7383237B2 - Computer-aided image analysis - Google Patents
Computer-aided image analysis Download PDFInfo
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- US7383237B2 US7383237B2 US11/349,542 US34954206A US7383237B2 US 7383237 B2 US7383237 B2 US 7383237B2 US 34954206 A US34954206 A US 34954206A US 7383237 B2 US7383237 B2 US 7383237B2
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0012—Biomedical image inspection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/211—Selection of the most significant subset of features
- G06F18/2113—Selection of the most significant subset of features by ranking or filtering the set of features, e.g. using a measure of variance or of feature cross-correlation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2411—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/25—Fusion techniques
- G06F18/254—Fusion techniques of classification results, e.g. of results related to same input data
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
- G06N20/10—Machine learning using kernel methods, e.g. support vector machines [SVM]
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H30/00—ICT specially adapted for the handling or processing of medical images
- G16H30/40—ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V2201/00—Indexing scheme relating to image or video recognition or understanding
- G06V2201/03—Recognition of patterns in medical or anatomical images
Abstract
Description
TABLE 1 | |||
ACRONYM | DESCRIPTION | ||
ATAPI | attachment packet interface | ||
CT | computed tomography | ||
DMA | direct memory access | ||
EIDE | enhanced integrated drive electronics | ||
FFT | fast Fourier transform | ||
I/O | input/output | ||
IDE | integrated drive electronics | ||
LAN | local area network | ||
MRI | magnetic resonance imagining | ||
PET | positron emission tomography | ||
RAM | random access memory | ||
ROM | read-only memory | ||
SCSI | small computer system interface | ||
SPECT | single-photon emission computed tomography | ||
SVM | support vector machine | ||
WAN | wide area network | ||
TABLE 2 | ||
Vapnik's Polynomial | ||
Alphas bounded up to 1000 | ||
Input values will be individually scaled to lie between 0 and 1 | ||
SV zero threshold: 1e−16 | ||
Margin threshold: 0.1 | ||
Objective zero tolerance: 1e−17 | ||
Degree of polynomial: 2 | ||
Test set: | ||
Total samples: 24 | ||
Positive samples: 8 | ||
False negatives: 4 | ||
Negative samples: 16 | ||
False positives: 6 | ||
The test output has been post-processed to be comprehensible by a human or computer. According to the table, the test output shows that 24 total samples (data points) were examined by the SVM and that the SVM incorrectly identified four of eight positive samples (50%), i.e., found negative for a positive sample, and incorrectly identified 6 of sixteen negative samples (37.5%), i.e., found positive for a negative sample.
TABLE 3 | ||
Vapnik's Polynomial | ||
Alphas bounded up to 1000 | ||
Input values will be individually scaled to lie between 0 and 1 | ||
SV zero threshold: 1e−16 | ||
Margin threshold: 0.1 | ||
Objective zero tolerance: 1e−17 | ||
Degree of polynomial: 2 | ||
Test set: | ||
Total samples: 24 | ||
Positive samples: 8 | ||
False negatives: 4 | ||
Negative samples: 16 | ||
False positives: 4 | ||
The expanded test output has been post-processed to be comprehensible by a human or computer. As indicated, the expanded test output shows that 24 total samples (data points) were examined by the SVM and that the SVM incorrectly identified four of eight positive samples (50%) and incorrectly identified four of sixteen negative samples (25%). Accordingly, by comparing this expanded test output with the unexpanded test output of Table 2, it may be seen that the expansion of the data points leads to improved results (i.e. a lower global minimum error), specifically a reduced instance of patients who would unnecessarily be subjected to follow-up cancer treatments.
TABLE 4 | ||
Number of subintervals: 2 | ||
Number of classes: 2 | ||
Number of data points: 46 | ||
Lower bound: −1 | ||
Upper bound: 10 | ||
Number of bins: 22 | ||
Regularization constant: 1 | ||
Data file: posnodes.prn | ||
Min. Entropy −0.568342 | ||
Optimal cut-off: 5.500000 | ||
TABLE 5 | ||||
Correctly Classified | Correctly Classified | |||
Cut Point | Recurrence | Non-Recurrence | ||
Clinical (≧3.0) | 7 of 15 (47%) | 22 of 31 (71%) | ||
Optimal (≧5.5)) | 5 of 15 (33%) | 30 of 31 (97%) | ||
Accordingly, 53% of the recurrences were incorrectly classified (further treatment was improperly not recommended) and 29% of the non-recurrences were incorrectly classified (further treatment was incorrectly recommended). By contrast, the cutoff point determined by the optimal categorization method 300 (≧5.5) resulted in 33% correctly classified recurrences and 97% correctly classified non-recurrences. Accordingly, 67% of the recurrences were incorrectly classified (further treatment was improperly not recommended) and 3% of the non-recurrences were incorrectly classified (further treatment was incorrectly recommended).
TABLE 6 | |||
I. Simple Dot Product | II. Vapnik's Polynomial | ||
Alphas bounded up to 1000. | Alphas bounded up to 1000. | ||
Input values will not be scaled. | Input values will not be scaled. | ||
SV zero threshold: 1e−16 | SV zero threshold: 1e−16 | ||
Margin threshold: 0.1 | Margin threshold: 0.1 | ||
Objective zero tolerance: 1e−07 | Objective zero tolerance: 1e−07 | ||
Degree of polynomial: 2 | |||
Test set | Test set | ||
Total samples: 24 | Total samples: 24 | ||
Positive samples: 8 | Positive samples: 8 | ||
False negatives: 6 | False negatives: 2 | ||
Negative samples: 16 | Negative samples: 16 | ||
False positives: 3 | False positives: 4 | ||
Table 6 demonstrates that a variation in the selection of a kernel may affect the level of quality of the output of a SVM. As shown, the post-processed output of a first SVM (Column I) comprising a linear dot product kernel indicates that for a given test set of twenty four samples, six of eight positive samples were incorrectly identified and three of sixteen negative samples were incorrectly identified. By way of comparison, the post-processed output for a second SVM (Column II) comprising a polynomial kernel indicates that for the same test set, only two of eight positive samples were incorrectly identified and four of sixteen negative samples were identified. By way of comparison, the polynomial kernel yielded significantly improved results pertaining to the identification of positive samples and yielded only slightly worse results pertaining to the identification of negative samples. Thus, as will be apparent to those of skill in the art, the global minimum error for the polynomial kernel is lower than the global minimum error for the linear kernel for this data set.
-
- E1 Find the border pixels.
- E2 Keep the kernel pixels which are common to opposite borders (left and right borders or top and bottom borders).
- E3 Delete the kernel pixels belonging to the upper border.
- E4 Find the border pixels.
- E5 Delete the border pixels belonging to the right border.
- E6 Find the border pixels.
- E7 Delete the border pixels belonging to the bottom border.
- E8 Find the border pixels.
- E9 Delete the border pixels belonging to the left border.
- E10 Return to point E1 and repeat all steps until all pixels on the bitmap are kernel pixels.
-
- 1. Compute the
centroid 902 of thecalcification 900. - 2. Use the
centroid 902 as the origin of a polar coordinate system and sample the contour of the calcification with n equally spaced angles. This gives n radial measures 904 which form an n dimensional vector [r1,r2,K,rn,]. - 3. Apply a discrete Fourier transform to the vector obtained in
step 2. The resulting n-dimensional complex vector is used as the input to the SVM.
- 1. Compute the
-
- 1. Given a contour that is a Jordan (simple continuous closed) curve in the plane, represent the contour as a complex-valued function z(s), 0≦s≦1. Regard the origin of the complex plane at the centroid of the contour and associate the points on the contour with the complex numbers of the function.
- 2. Compute the Fourier coefficients of z(s) up to order N.
-
- 3. For two contours z(s), w(s) with Fourier coefficients fn, gn, the kernel is defined as
subject to the constraints
The constant C is selected to penalize the misclassified points.
subject to the constraints
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- 1. Construct two graphs representing the dense areas in the two images under comparison.
- 2. Find an optimal matching between the vertices of two graphs.
- 3. Evaluate the mismatched vertices and eliminate the ones that can be merged into adjacent vertices within acceptable variations.
- 4. The remaining mismatched vertices represent the asymmetric densities.
Claims (20)
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US11/349,542 US7383237B2 (en) | 1998-05-01 | 2006-02-06 | Computer-aided image analysis |
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US8396198P | 1998-05-01 | 1998-05-01 | |
US09/303,387 US6128608A (en) | 1998-05-01 | 1999-05-01 | Enhancing knowledge discovery using multiple support vector machines |
US16180699P | 1999-10-27 | 1999-10-27 | |
US16870399P | 1999-12-02 | 1999-12-02 | |
US18459600P | 2000-02-24 | 2000-02-24 | |
US19121900P | 2000-03-22 | 2000-03-22 | |
US09/568,301 US6427141B1 (en) | 1998-05-01 | 2000-05-09 | Enhancing knowledge discovery using multiple support vector machines |
US09/578,011 US6658395B1 (en) | 1998-05-01 | 2000-05-24 | Enhancing knowledge discovery from multiple data sets using multiple support vector machines |
US20702600P | 2000-05-25 | 2000-05-25 | |
US09/633,410 US6882990B1 (en) | 1999-05-01 | 2000-08-07 | Methods of identifying biological patterns using multiple data sets |
US26338101P | 2001-01-23 | 2001-01-23 | |
US10/056,438 US6996549B2 (en) | 1998-05-01 | 2002-01-23 | Computer-aided image analysis |
US11/349,542 US7383237B2 (en) | 1998-05-01 | 2006-02-06 | Computer-aided image analysis |
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US09/578,011 Continuation-In-Part US6658395B1 (en) | 1998-05-01 | 2000-05-24 | Enhancing knowledge discovery from multiple data sets using multiple support vector machines |
US09/633,410 Continuation-In-Part US6882990B1 (en) | 1998-05-01 | 2000-08-07 | Methods of identifying biological patterns using multiple data sets |
US10/056,438 Continuation US6996549B2 (en) | 1998-05-01 | 2002-01-23 | Computer-aided image analysis |
US11/349,542 Continuation US7383237B2 (en) | 1998-05-01 | 2006-02-06 | Computer-aided image analysis |
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US20060224539A1 (en) | 2006-10-05 |
US6996549B2 (en) | 2006-02-07 |
US20020165837A1 (en) | 2002-11-07 |
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